Master Maker: Understanding Gaming Skill Through Practice and Habit From Gameplay Behavior

• Published in: Topics in cognitive science 2017-04, Vol.9 (2), p.437-466
• Main Authors: Huang, Jeff, Yan, Eddie, Cheung, Gifford, Nagappan, Nachiappan, Zimmermann, Thomas
• Format: Article
• Language: English
• Subjects: Analytics; Gameplay data; Habit; Habits; Halo; Humans; Practice; Starcraft; Time‐series; Video Games
• ISSN: 1756-8757; 1756-8765
• DOI: 10.1111/tops.12251

The study of expertise is difficult to do in a laboratory environment due to the challenge of finding people at different skill levels and the lack of time for participants to acquire mastery. In this paper, we report on two studies that analyze naturalistic gameplay data using cohort analysis to better understand how skill relates to practice and habit. Two cohorts are analyzed, each from two different games (Halo Reach and StarCraft 2). Our work follows skill progression through 7 months of Halo matches for a holistic perspective, but also explores low‐level in‐game habits when controlling game units in StarCraft 2. Players who played moderately frequently without long breaks were able to gain skill the most efficiently. What set the highest performers apart was their ability to gain skill more rapidly and without dips compared to other players. At the beginning of matches, top players habitually warmed up by selecting and re‐selecting groups of units repeatedly in a meaningless cycle. They exhibited unique routines during their play that aided them when under pressure. Big Data harvested from online records of two popular video games provides insights into two problems that are hard to study in most laboratories. Cohort analysis is used to group players by the date they first played Halo Reach™ to determine that those who advance the most in the game engaged in “group deliberate practice.” In part 2, StarCraft 2™ is used to investigate the role of “habitual game actions” in situations in which these actions are superfluous.